
"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    BertTokenizerFast,
					    BlipImageProcessor,
					    GPTaTokenizer,
					    InstructBlipProcessor,
					    PreTrainedTokenizerFast,
					)




@require_vision
class 					lowercase__(					unittest.TestCase ):
	'''simple docstring'''

	def 	__lowerCAmelCase			(    self     :int       )				->      Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  List[Any]					=			tempfile.mkdtemp()

			SCREAMING_SNAKE_CASE							:  Any					=			BlipImageProcessor()
			SCREAMING_SNAKE_CASE							:  Optional[int]					=			GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''       )
			SCREAMING_SNAKE_CASE							:  Tuple					=			BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''       )

			SCREAMING_SNAKE_CASE							:  Dict					=			InstructBlipProcessor(_lowercase		,			_lowercase		,			_lowercase       )

			processor.save_pretrained(self.tmpdirname       )





	def 	__lowerCAmelCase			(    self     :int		,			**lowerCamelCase_     :List[Any]       )				->      Tuple:







			'''simple docstring'''




			return AutoProcessor.from_pretrained(self.tmpdirname		,			**_lowercase       ).tokenizer





	def 	__lowerCAmelCase			(    self     :Optional[Any]		,			**lowerCamelCase_     :str       )				->      List[Any]:







			'''simple docstring'''




			return AutoProcessor.from_pretrained(self.tmpdirname		,			**_lowercase       ).image_processor





	def 	__lowerCAmelCase			(    self     :Optional[int]		,			**lowerCamelCase_     :int       )				->      Optional[int]:







			'''simple docstring'''




			return AutoProcessor.from_pretrained(self.tmpdirname		,			**_lowercase       ).qformer_tokenizer





	def 	__lowerCAmelCase			(    self     :List[str]       )				->      List[str]:







			'''simple docstring'''




			shutil.rmtree(self.tmpdirname       )





	def 	__lowerCAmelCase			(    self     :List[str]       )				->      List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  int					=			[np.random.randint(2_55		,			size=(3, 30, 4_00)		,			dtype=np.uinta       )]

			SCREAMING_SNAKE_CASE							:  Optional[Any]					=			[Image.fromarray(np.moveaxis(_lowercase		,			0		,			-1       )       ) for x in image_inputs]

			return image_inputs





	def 	__lowerCAmelCase			(    self     :str       )				->      Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  List[str]					=			InstructBlipProcessor(
			    tokenizer=self.get_tokenizer()		,			image_processor=self.get_image_processor()		,			qformer_tokenizer=self.get_qformer_tokenizer()		,			)
			processor.save_pretrained(self.tmpdirname       )

			SCREAMING_SNAKE_CASE							:  List[Any]					=			self.get_tokenizer(bos_token='''(BOS)'''		,			eos_token='''(EOS)'''       )
			SCREAMING_SNAKE_CASE							:  str					=			self.get_image_processor(do_normalize=_lowercase		,			padding_value=1.0       )

			SCREAMING_SNAKE_CASE							:  List[str]					=			InstructBlipProcessor.from_pretrained(
			    self.tmpdirname		,			bos_token='''(BOS)'''		,			eos_token='''(EOS)'''		,			do_normalize=_lowercase		,			padding_value=1.0       )

			self.assertEqual(processor.tokenizer.get_vocab()		,			tokenizer_add_kwargs.get_vocab()       )
			self.assertIsInstance(processor.tokenizer		,			_lowercase       )

			self.assertEqual(processor.image_processor.to_json_string()		,			image_processor_add_kwargs.to_json_string()       )
			self.assertIsInstance(processor.image_processor		,			_lowercase       )
			self.assertIsInstance(processor.qformer_tokenizer		,			_lowercase       )





	def 	__lowerCAmelCase			(    self     :Tuple       )				->      str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  Optional[int]					=			self.get_image_processor()
			SCREAMING_SNAKE_CASE							:  str					=			self.get_tokenizer()
			SCREAMING_SNAKE_CASE							:  Dict					=			self.get_qformer_tokenizer()

			SCREAMING_SNAKE_CASE							:  List[str]					=			InstructBlipProcessor(
			    tokenizer=_lowercase		,			image_processor=_lowercase		,			qformer_tokenizer=_lowercase       )

			SCREAMING_SNAKE_CASE							:  int					=			self.prepare_image_inputs()

			SCREAMING_SNAKE_CASE							:  Any					=			image_processor(_lowercase		,			return_tensors='''np'''       )
			SCREAMING_SNAKE_CASE							:  str					=			processor(images=_lowercase		,			return_tensors='''np'''       )

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()		,			input_processor[key].sum()		,			delta=1E-2       )





	def 	__lowerCAmelCase			(    self     :Any       )				->      Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  Any					=			self.get_image_processor()
			SCREAMING_SNAKE_CASE							:  Optional[int]					=			self.get_tokenizer()
			SCREAMING_SNAKE_CASE							:  str					=			self.get_qformer_tokenizer()

			SCREAMING_SNAKE_CASE							:  Tuple					=			InstructBlipProcessor(
			    tokenizer=_lowercase		,			image_processor=_lowercase		,			qformer_tokenizer=_lowercase       )

			SCREAMING_SNAKE_CASE							:  List[Any]					=			"""lower newer"""

			SCREAMING_SNAKE_CASE							:  int					=			processor(text=_lowercase       )

			SCREAMING_SNAKE_CASE							:  Dict					=			tokenizer(_lowercase		,			return_token_type_ids=_lowercase       )
			SCREAMING_SNAKE_CASE							:  Any					=			qformer_tokenizer(_lowercase		,			return_token_type_ids=_lowercase       )

			for key in encoded_tokens.keys():
					self.assertListEqual(encoded_tokens[key]		,			encoded_processor[key]       )

			for key in encoded_tokens_qformer.keys():
					self.assertListEqual(encoded_tokens_qformer[key]		,			encoded_processor['''qformer_''' + key]       )





	def 	__lowerCAmelCase			(    self     :Union[str, Any]       )				->      List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  Optional[Any]					=			self.get_image_processor()
			SCREAMING_SNAKE_CASE							:  str					=			self.get_tokenizer()
			SCREAMING_SNAKE_CASE							:  Dict					=			self.get_qformer_tokenizer()

			SCREAMING_SNAKE_CASE							:  int					=			InstructBlipProcessor(
			    tokenizer=_lowercase		,			image_processor=_lowercase		,			qformer_tokenizer=_lowercase       )

			SCREAMING_SNAKE_CASE							:  Dict					=			"""lower newer"""
			SCREAMING_SNAKE_CASE							:  Optional[Any]					=			self.prepare_image_inputs()

			SCREAMING_SNAKE_CASE							:  str					=			processor(text=_lowercase		,			images=_lowercase       )

			self.assertListEqual(
			    list(inputs.keys()       )		,			['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']		,			)

			# test if it raises when no input is passed
			with pytest.raises(_lowercase       ):
					processor()





	def 	__lowerCAmelCase			(    self     :Tuple       )				->      Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  int					=			self.get_image_processor()
			SCREAMING_SNAKE_CASE							:  int					=			self.get_tokenizer()
			SCREAMING_SNAKE_CASE							:  Tuple					=			self.get_qformer_tokenizer()

			SCREAMING_SNAKE_CASE							:  Dict					=			InstructBlipProcessor(
			    tokenizer=_lowercase		,			image_processor=_lowercase		,			qformer_tokenizer=_lowercase       )

			SCREAMING_SNAKE_CASE							:  Optional[int]					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			SCREAMING_SNAKE_CASE							:  int					=			processor.batch_decode(_lowercase       )
			SCREAMING_SNAKE_CASE							:  Any					=			tokenizer.batch_decode(_lowercase       )

			self.assertListEqual(_lowercase		,			_lowercase       )







	def 	__lowerCAmelCase			(    self     :Any       )				->      Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE							:  str					=			self.get_image_processor()
			SCREAMING_SNAKE_CASE							:  Dict					=			self.get_tokenizer()
			SCREAMING_SNAKE_CASE							:  Optional[int]					=			self.get_qformer_tokenizer()

			SCREAMING_SNAKE_CASE							:  Optional[int]					=			InstructBlipProcessor(
			    tokenizer=_lowercase		,			image_processor=_lowercase		,			qformer_tokenizer=_lowercase       )

			SCREAMING_SNAKE_CASE							:  List[str]					=			"""lower newer"""
			SCREAMING_SNAKE_CASE							:  Union[str, Any]					=			self.prepare_image_inputs()

			SCREAMING_SNAKE_CASE							:  Union[str, Any]					=			processor(text=_lowercase		,			images=_lowercase       )

			self.assertListEqual(
			    list(inputs.keys()       )		,			['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']		,			)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


